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Journal of power sources 521, 230825 (2022). doi:10.1016/j.jpowsour.2021.230825, Published by Elsevier, New York, NY [u.a.]

The use of reduced-order electrochemical models creates opportunities for battery management systems to control the battery behavior by monitoring the internal states in electrochemical processes, which are critical for safety enhancement and degradation mitigation. This paper explores a state observer for lithium-ion batteries based on an extended single-particle model, which results in a trade-off between high accuracy and low computational burden, thus enables the real-time application. An adaptive unscented Kalman filter based on this model is developed to estimate not only the state of charge but also lithium-ion concentrations and potentials, which precisely describe battery internal behaviors to avoid lithium plating. Experimental tests are carried out with a lithium-ion battery cell for both model and state estimation validations. Furthermore, the estimation accuracies of the unmeasurable states are also verified by numerical validation tests with a high-fidelity electrochemical model. All estimated states present fast convergence, robustness, and high accuracy despite a 20% initial state-of-charge error. Additionally, the enhancement in the state estimation accuracy and robustness by the new noise adaption step is demonstrated by an application-relevant evaluation framework, considering sensor noise, state uncertainty, parameter uncertainty, and computation time.

Fast charging capability is considered a critical factor for the widespread adoption of electric vehicles. High charging currents can, however, severely affect battery health due to the danger of metallic lithium deposition on the anode and consequent degradation reactions. The charging speed should therefore be limited with respect to battery temperature, state of charge, and cell design, governing the onset point of lithium plating. Electrochemical models are a suitable tool providing continuous estimates of the anode potential as the main lithium plating indicator while covering a wide operational range. In this article, we present a novel charging control scheme based on a real-time capable simulation framework with adjustable model resolution. A profound investigation of error sources and modeling uncertainties motivates online state corrections towards a lower bound of the anode potential, which are realized by selective adjustments of the lithium distribution within electrode particles based on the full cell voltage error. Simulations of controlled and conventional CC charging profiles indicate the importance of continuously adapting the charging power for different operating conditions. Further, simulated state and parameter distortions as they might result from initialization and parameterization errors show that our estimation strategy can mitigate the risk of unsafe control operations.

Journal of the Electrochemical Society : JES 167(9), (2020). doi:10.1149/1945-7111/ab8f5a special issue: "JES Focus Issue on Battery Safety, Reliability and Mitigation", Published by IOP Publishing, Bristol

The knowledge of the dynamic available charging and discharging power of the battery is a piece of essential information for the safety and longevity of the battery energy storage systems. An accurate real-time prediction of these quantities is very challenging due to the high nonlinearities of battery dynamics. In this paper, an electrochemical model-based online state-of-power prediction algorithm under different time horizons is developed for a safer and more reliable operation of lithium-ion batteries. The safety constraints, which define the safety operation area for the power prediction, are designed based on not only the terminal voltage but also battery internal electrochemical states, i.e., the electrode surface concentration, the electrolyte concentration, and the side reaction overpotential. The algorithm is validated by simulations and experiments under a dynamic load profile, and the dominating constraints in charging and discharging as well as the influence of predictive time horizons on the available battery power are analyzed, providing important information for further researches. Furthermore, the computational speed of the proposed iterative algorithm is improved with the integration of Gaussian process regression by up to 50%. A comparative study with a state-of-the-art equivalent circuit model-based approach highlights the significant benefits of the proposed electrochemical model-based algorithm in operation safety enhancement and battery performance improvement.

An accurate estimation of the internal states of lithium-ion batteries is critical to improving the reliability and durability of battery systems. Data-driven methods have exhibited enormous potential for precisely capturing electric and thermal cell dynamics with a low computational cost. However, challenges remain regarding accurate and low-cost data acquisition as electrode-level states are unmeasurable with conventional sensors. This paper presents a hybrid state estimation method for lithium-ion batteries integrating physics-based and machine learning models to leverage their respective strengths. An electrochemical-thermal model is developed and experimentally verified, which is employed to generate a large quantity of data, i.e., voltage, current, temperature and internal electrochemical states, under a comprehensive operating condition matrix including various load profiles and temperatures. These data are fed to train a deep neural network to estimate the internal concentrations and potentials in the electrodes and the electrolyte at different spatial positions. The results show that the proposed approach is capable of bridging spatial, temporal and chemical complexity and achieves a maximum error of 2.93% for all the estimated states under new ambient temperatures, indicating high reliability and generalization ability with solid robustness to input noises and outperforming the one-dimensional network under both normal and noisy conditions.

This work conducts a post-mortem analysis of a cycled commercial lithium-ion pouch cell under an induced inhomogeneous pressure by using a stainless-steel sphere as a force transmitter to induce an inhomogeneous pressure distribution on a cycled lithium-ion battery. After the cycling, a macroscopic and microscopic optical analysis of the active and passive materials was executed. Also, scanning electron microscopy was used to analyze active material particles. The sphere shape results in a heterogenic pressure distribution on the lithium-ion battery and induces a ring of locally high electrochemical activity, which leads to lithium plating. Furthermore, a surface layer found on the anode, which is a possible cause of electrolyte degradation at the particle&ndash, electrolyte interface. Significant deformation and destruction of particles by the local pressure was observed on the cathode. The analysis results validate previous simulations and theories regarding lithium plating on edge effects. These results show that pressure has a strong influence on electrolyte-soaked active materials.

Accurate identification of physical parameters of a lithium-ion electrochemical model is of critical importance for next-generation battery management systems. The complexity of the electrochemical model increases the difficulty of the identification process, and hence the analysis of parameter identifiability is the cornerstone for accurate parameter identification. The overarching goal of this paper is to analyze the parameter sensitivity of an electrochemical model under both the charging process and real-world driving cycles. The boundaries for the sensitivity analysis of 26 physical parameters are determined with a systematic benchmarking of published parameters for lithium Nickel-Manganese-Cobalt-Oxide/graphite cells. In particular, the sensitivity of the parameters is analyzed not only for terminal voltage but also for essential states in an electrochemical model-based battery management system, e.g., cathode bulk state of charge, cathode surface state of charge and anode potential. The sensitivity matrices of the parameters under different C-rates and depth of discharge regions clearly show their different influences on capacity-related parameters and other parameters. Furthermore, the rankings of the normalized parameter sensitivity index provide us the identifiability of the parameters, as well as the influence of parameter inaccuracy on the main functionalities in an electrochemical model-based battery management system.

Applied Sciences 8(4), 534 (2018). doi:10.18154/RWTH-2017-08674, Published by MDPI, Basel